In modem life, there are a number of devices, notably digital computers and multifunctional handheld units that involve data entry, typically text, including for example cellular phones and other devices like organizers and handheld computers. For all of these, one important use is the entry of linguistic items like words, phrases, and sentences. For example, a user may create an unstructured text document or might formulate an email message or a short text message to be sent as an SMS message on a cell phone. In such cases, text entry may occur through use of a keyboard or stylus for some handheld computers or cell phones, etc. However, data entry can be difficult when the keyboard is relatively small as it is on a handheld cell phone, organizer or computer, or uses individual keys for entry of multiple letters, text, especially when a large number of characters must be entered. Similarly, with devices employing a stylus for text entry, entry of text can be slow and burdensome.
Automated word completion programs have eased the burden somewhat. Such automated word completion programs have appeared recently in a variety of applications in a variety of devices. These programs are typically based on either predefined word suggestion lists (e.g. a dictionary) or are culled from the user's own most recently typed terms, the latter often called MRU (i.e. “Most Recently Used”) programs. For example, the former type of program is based on a pre-given word suggestion list based on a dictionary augmented with information about which words are more frequently used. If a user types the characters “su” in a document, then it might suggest “super” as the appropriate word completion based on the fact that it belongs to the pre-given word suggestion list and has a high frequency of use in general English. On the other hand, the latter type of program would suggest a word completion based on the user's own recently used words (e.g. “supreme” may be suggested to a lawyer who has recently input “Supreme Court”). Such programs are often found in web browsers for example and will suggest the most recently used “uniform resource locator” or URL (e.g. www.google.com when the user types “www.g”) as characters are input.
A third type of program is able to detect that the user is in a particular type of field (e.g. the closing of a letter) and will suggest word completions (e.g. “Sincerely” when the user types “Si”) based on a more limited “contextual” list. An extension of this is to maintain many separate word suggestion lists and allow the user to choose an appropriate list for each document the user creates. Other variants allow users to actually insert entries manually into word suggestion lists (e.g. a name and address) or to maintain frequencies of word usage by a user and thus, rather than offering the most recently used word, offer the user's most frequently used words.